Search Results (3,208)

Greenways offer sustainable benefits at ecological, cultural and economic levels, enhancing human well-being. Landscape performance assessment is a crucial task for evaluating these benefits and guiding the sustainable development of greenways. To clarify the characteristics and roles of landscape performance in the development of the US greenway system, text analysis was conducted using KH Coder, and a meta-analysis was performed on three databases to select research cases on greenway performance evaluation in the US. The results show that the evaluation of social performance is higher than that of ecological and economic performances, and the data related to economic performance is more difficult to obtain. The efficacy of greenway projects varies with the construction stage and is influenced by social background and target benefits. The sustainability characteristics of high co-occurrence relationships are key to guiding greenway performance assessment, which helps in selecting indicators for evaluating greenways and providing references for improving planning directions. In the future, innovative technical means tailored to the goals of greenway landscapes should be used for performance evaluation. Full article

Background: As diabetes mellitus continues to escalate into a global health crisis, particularly in China, the limitations of conventional pharmacotherapy underscore the need for complementary interventions. This study systematically reviews two decades of research progress on acupuncture for diabetes management. Methods: A total of 391 publications met the inclusion criteria from the Web of Science Core Collection (2004–2024) using the search terms “acupuncture” AND “diabetes”. These comprised 294 original studies and 97 reviews. CiteSpace 6.3.R1 was used to perform multidimensional analyses, including co-occurrence networks, centrality algorithms, and silhouette metrics across countries/regions, institutions, authors, journals, references, and keywords. Results: The analysis shows a significant increase in publications on acupuncture for diabetes management after 2013. China and the United States lead in research output, yet collaboration between the two countries remains limited. Most researchers currently work within isolated clusters, underscoring the need for greater exchanges and cooperation. Furthermore, this study identified three key research hotspots: insulin resistance, complications, and interdisciplinary research. Conclusions: This bibliometric analysis reveals dynamic growth patterns and paradigm shifts in acupuncture and diabetes research. The findings provide valuable implications for integrating acupuncture into diabetes treatment. Full article

Objectives: Periapical periodontitis, which is a periodontal dysfunction, is a current clinical problem. Due to the frequency of occurrence and the adverse effects they cause, they are considered a social disease. They require detailed diagnostics to implement appropriate treatment. Mathematical calculations based on data obtained from radiological images used in routine clinical practice may help differentiate the forms of periodontitis. This study aimed to evaluate the areas affected by periodontitis in comparison to the healthy tissues of the periapical area. Methods: The study analyzed texture components using the gray-level co-occurrence matrix (GLCM) and the gray-level run-length matrix (GRLM) on an orthopantomogram (OPG) from 50 patients with clinically confirmed periodontitis treated at the Department of Maxillofacial and Plastic Surgery, University of Bialystok. Texture analysis was performed on defined regions of interest (ROIs) to distinguish diseased from healthy tissues. We employed four classification algorithms to assess model performance. Results: The data set included 50 patients, with 76 cases of periodontitis and 50 healthy ROIs. The reference standard was clinical diagnosis confirmed by two specialist doctors. The best-performing algorithm achieved an AUC of 98%. Conclusions: The obtained results showed significant statistical differences in the inflamed regions compared to the control, which may aid in diagnosing and selecting the treatment method for periodontitis. Full article

The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in drama. We propose a novel method to supplement addressee identification in tragedies using Large Language Models (LLMs). Unlike conventional Social Network Analysis (SNA) approaches, which often diminish dialogue dynamics by relying on co-occurrence or adjacency heuristics, our LLM-based method accurately records directed speech acts, joint addresses, and listener interactions. In a preliminary evaluation of an annotated multilingual dataset of 14 scenes from nine plays in four languages, our top-performing LLM (i.e., Llama3.3-70B) achieved an F1-score of $88.75\%$ (P = $94.81\%$, R = $84.72\%$), an exact match of 77.31%, and an 86.97% partial match with human annotations, where partial match indicates any overlap between predicted and annotated receiver lists. Through automatic extraction of speaker–addressee relations, our method provides preliminary evidence for the potential scalability of SNA for literary analyses, as well as insights into power relations, influence, and isolation of characters in tragedies, which we further visualize by rendering social network graphs. Full article

The cultural landscape of Gubeikou, with distinct historical stratification and event-relatedness, bears unique value. Against the backdrop of increasingly prominent themes of cultural heritage development and transformation, research on Gubeikou’s cultural landscapes remains fragmented and lacking in depth. This research explores its evolution mechanism via historical events to fill gaps. This study takes Gubeikou Town as the research object, applies the text analysis method to sort and categorize 302 historical events, summarizes 12 event types, identifies 19 landscape elements, and constructs a data matrix based on co-occurrence frequencies. It performs clustering analysis on these using Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC), while integrating historical and geographical data. Findings: (1) The landscape evolution of Gubeikou can be divided into four main stages: the military embryonic period, the functional expansion period, the system maturity period, and the multi-element integration period. (2) The dynamic evolutionary trajectory of the correlation between its landscapes and events shows that the core factors affecting the evolution of cultural landscapes in each period not only maintain the dominance of military elements throughout the evolutionary process but also integrate diverse elements like economy, culture, and folk customs with social development, presenting the characteristics of composite evolution. (3) The landscape evolution is driven by the “primary–secondary synergy” dynamic structure composed of four types of activities: military–political, transportation, production–trade, and construction. It is the product of the coupling effect of political goals, social operation, and geographical conditions. This study provides a basis for the sustainable protection and utilization of Gubeikou, and also offers a reference for other regions. Full article

Funerary archaeoentomology is the discipline that studies insects and other arthropods in archaeological contexts, with a particular focus on the funerary domain. The presence of specific species, such as necrophagous beetles or saprophagous flies, can provide crucial evidence regarding post-mortem conditions—whether bodies were left exposed to the air or buried suddenly after death—and whether they underwent particular preservation practices, such as desiccation or embalming. This study concentrates on entomological specimens collected from three mummified bodies at the Sanctuary of Madonna della Corona in the province of Verona (northeast Italy), aiming to reconstruct aspects of funerary practices, especially the season of death and the authenticity of the garments worn by the mummified individuals. Insects were manually collected from bodies belonging to three hermits living between the 17th and 19th centuries. A complex entomofauna consisting of Diptera, Coleoptera, Lepidoptera, and minor taxa was collected and analyzed. Diptera puparia, primarily from the families Calliphoridae, Muscidae, and Fanniidae, were the most abundant entomological elements recovered. Their presence suggests potential exposure of the bodies before burial and indicates that death likely occurred during a mild period of the year (end of spring/beginning of autumn). The co-occurrence of holes caused by maggots on the hermits’ skin and their garments allows us to speculate about the authenticity of the clothing used during the funerary rituals. By combining entomological evidence with textile analysis, this research offers a more precise understanding of historical funerary practices within this devotional context. It sheds light on methods of managing human remains, burial traditions, and preservation techniques, particularly regarding the clothing of the deceased. Full article

The highly fatal rabbit hemorrhagic disease (RHD) that first emerged in 1984 in China has spread worldwide and affects both domestic and wild rabbits. The disease was originally caused by RHD virus (Lagovirus europaeus, L.europaeus) of GI.1 genotype, but over the years, two further pathogenic forms, known as the antigenic and genetic variant RHDVa (GI.1a) and RHDV2 (genotype GI.2), have been identified. RHD was first reported in Poland in 1988, when two RHDV strains were isolated, currently classified as GI.1c, while RHDVa and RHDV2 emerged in 2003 and 2016, respectively. In this study, using virological and molecular methods, we characterized five new RHDV strains belonging to GI.1 (RHDV)/GI.1a (RHDVa) and GI.2 (RHDV2) genotypes isolated in Poland in 2020–2022, in domestic rabbits from backyard farm and companion animals. We showed that two strains of L. europaeus (NRU 2020 and LIB 2020) from 2020 in the phylogenies of nonstructural proteins (NSP) and structural capsid protein (SP-VP60) clustered in a homogeneous GI.1a variant group. We stated that three strains of L. europaeus from 2020 to 2022 (KOB 2020, ZWO 2021, WAE 2022) in the VP60 phylogeny were positioned in the GI.2 (RHDV2) genotype, while in the NSP phylogeny, they are genetically related to recombinants with the GI.3/GI.2 genotype. Unexpectedly, in two RHD cases identified in the same small geographical area of south-eastern Poland (Libusza and Kobylanka), the close coexistence of RHDVa (LIB2020) and RHDV2 (KOB2020) strains capable of causing independent infections at the same time was found. This leads to the conclusion that the close natural coexistence of RHDV strains belonging to different genotypes does not necessarily have to directly lead to the emergence of new genetic or antigenic variants, which confirms the distinctness of both genetic forms and indicates different evolutionary paths leading to the best possible adaptation to the host. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

Agricultural pesticides are significant drivers of antibiotic resistance in soil. However, the differential impacts of antibiotic versus non-antibiotic pesticides on the soil resistome are poorly characterized. Here, we analyzed sequencing data from soils exposed to either antibiotic or non-antibiotic pesticides to compare differences in antibiotic resistance gene (ARG) burden, diversity, assembly processes, network topology, and host taxonomy. Soils exposed to antibiotic pesticides exhibited a significantly higher ARG burden (0.52% vs. 0.27% of total genes), whereas soils exposed to non-antibiotic pesticides showed significantly higher alpha diversity (p < 0.05). ARG community compositions also differed significantly between antibiotic and non-antibiotic exposures (PERMANOVA, R² = 0.215, p < 0.001). Assembly analysis using the modified stochasticity ratio indicated that deterministic processes governed ARG community assembly in both groups, with stronger influence observed in non-antibiotic soils. Co-occurrence network analysis revealed contrasting patterns. A compact, highly centralized network emerged in antibiotic-exposed soils, while a larger, more dispersed network characterized non-antibiotic soils. In both networks, aminoglycoside ARGs served as keystone nodes, accompanied by the β-lactam ARG in antibiotic soils and the macrolide ARG in non-antibiotic soils. Pseudomonadota was the predominant ARG host (>60% contribution) across both exposures, though many other phyla exhibited significance (p < 0.05) between group differences in their ARG contributions. Non-pathogenic bacteria comprised the majority of ARG hosts in all samples. When examining ARG contributions from pathogenic hosts, zoonotic and animal-associated pathogens contributed significantly (p < 0.01) more in non-antibiotic soils than in antibiotic soils, whereas the ARG contribution from plant pathogens was comparable between the two pesticide groups. Overall, our study suggests that antibiotic and non-antibiotic pesticides shape distinct ARG network patterns and host–pathogen profiles, posing distinct risks to public health and agricultural ecosystems. Full article

Cereal contamination by mycotoxins represents a major food safety concern. This study aimed to assess the co-occurrence of 15 mycotoxins in 50 couscous samples marketed in Algeria using HPLC/FLD and LC-MS/MS techniques. The samples included various couscous types, differing in ingredients, production method (organic or conventional), processing operations, and granularity. The most frequently detected mycotoxins were tentoxin (76%), deoxynivalenol (74%), tenuazonic acid (72%), and ochratoxin A (54%). For the regulated mycotoxins, none of the concentrations exceeded the maximum levels set by the European Union. In contrast, tenuazonic acid and tentoxin, which are not yet regulated, were the most common compounds detected. Contamination with multiple mycotoxins was commonly observed: 90% of the samples contained at least two mycotoxins, with some containing up to seven. The most frequent combination involved tenuazonic acid-tentoxin-ochratoxin A. These findings highlight the need for frequent and systematic monitoring of couscous and other processed cereal-based products. Full article

Machine learning (ML) is reshaping how environmental chemicals are monitored and how their hazards are evaluated for human health. Here, we mapped this landscape by analyzing 3150 peer-reviewed articles (1985–2025) from the Web of Science Core Collection. Co-citation, co-occurrence, and temporal trend analyses in VOSviewer and R reveal an exponential publication surge from 2015, dominated by environmental science journals, with China and the United States leading in output. Eight thematic clusters emerged, centered on ML model development, water quality prediction, quantitative structure–activity applications, and per-/polyfluoroalkyl substances, with XGBoost and random forests as the most cited algorithms. A distinct risk assessment cluster indicates migration of these tools toward dose–response and regulatory applications, yet keyword frequencies show a 4:1 bias toward environmental endpoints over human health endpoints. Emerging topics include climate change, microplastics, and digital soil mapping, while lignin, arsenic, and phthalates appear as fast-growing but understudied chemicals. Our findings expose gaps in chemical coverage and health integration. We recommend expanding the substance portfolio, systematically coupling ML outputs with human health data, adopting explainable artificial intelligence workflows, and fostering international collaboration to translate ML advances into actionable chemical risk assessments. Full article

Background/Objectives: Sjögren’s disease (SjD) flares are incompletely understood. The patient perspective is critical to closing this gap. This retrospective social media listening (SML) study characterized the flare lexicon within the online Reddit SjD community using novel machine learning and natural language processing. Methods: Documents (posts/comments) were analyzed from the subreddit group “r/Sjogrens” (October 2012 to August 2023). Outcomes were as follows: (1) Frequency of documents mentioning flare, and contexts in which flare was mentioned; (2) clinical concepts associated with flare (analyzed using co-occurrence and pointwise mutual information [PMI]); (3) proportion of flare vs. non-flare documents relevant to SYMPTOMS or TESTING (compared using a two-proportion z-test); and (4) primary emotions mentioned in flare documents. Results: Of 59,266 documents with 5025 authors, flare was mentioned 3330 times (4.4% of documents from 19.1% of authors). Flare was discussed as a symptom (1423 instances), disease (13), or with no clinical category (1890). Flare-associated clinical concepts (co-occurrence > 100 and PMI² > 3) included SYMPTOMS (pain, fatigue, dryness of eye, xerostomia, arthralgia, stress) and BODY PARTS (eye, mouth, joints, whole body). More flare vs. non-flare documents mentioned a SYMPTOM, whereas fewer mentioned a TEST (p < 0.001 for both). Within flare documents, 36.5% expressed emotions, primarily fear (40.5% of primary emotions), happiness (17.8%), sadness (15.7%), and anger (15.5%). Conclusions: The SjD community discusses flare frequently and in context with symptoms, specifically pain, eye and mouth dryness, and fatigue. Flare conversations frequently involve negative emotions. Additional research is required to clarify the patient experience of flare, its clinical parameters, and implications. Full article

The continued acidification of red soil reduces phosphorus availability and microbial activity, which restricts corn growth. Phosphorus tailings, a waste product from phosphate mining, can neutralize soil acidity and supply controlled-release phosphorus, but their effects on the red soil-corn system remain unclear. A field experiment in Qujing, Yunnan (2023–2024), tested four treatments: CK (standard fertilization), T1 (CK plus phosphorus tailings), T2 (80% of standard fertilizer plus phosphorus tailings), and T3 (80% of standard fertilizer plus phosphorus tailings and organic fertilizer, both applied at 6.0 t·ha⁻¹). Using high-throughput sequencing, redundancy analysis (RDA), and structural equation modeling (SEM), the study evaluated impacts on soil properties, microbial communities, and corn yield and quality. Results showed: (1) Phosphorus tailings reduced soil acidification; T3 raised soil pH in the top 0–10 cm by 0.54–0.9 units compared to CK and increased total, available, and soluble phosphorus in the 0–20 cm layer to 952.82, 28.46, and 2.04 mg/kg, respectively. (2) T3 exhibited the highest microbial diversity (Chao1 and Shannon indices increased by 177.57% and 37.80% versus CK) and a more complex bacterial co-occurrence network (114 edges versus 107 in CK), indicating enhanced breakdown of aromatic compounds. (3) Corn yield under T3 improved by 13.72% over CK, with increases in hundred-grain weight (+6.02%), protein content (+18.04%), and crude fiber (+9.00%). (4) Effective nitrogen, ammonium nitrogen, available phosphorus, and soil conductivity were key factors affecting gcd/phoD phosphorus-reducing bacteria. (5) Phosphorus tailings indirectly increased yield by modifying soil properties and pH, both positively linked to yield, while gcd-carrying bacteria had a modest positive influence. In summary, combining phosphorus tailings with a 20% reduction in chemical fertilizer reduces fertilizer use, recycles mining waste, and boosts corn production in acidic red soil, though further studies are needed to evaluate long-term environmental effects. Full article

Major Depressive Disorder (MDD) represents a global challenge due to its high prevalence worldwide. Inflammation is the most extensively studied and plausible biological pathway involved in the onset of MDD. Individuals with MDD often exhibit low-grade inflammation, characterized by immune system dysregulation and activation of pro-inflammatory pathways. Elevated inflammation is also associated with a reduced response to antidepressant therapies, suggesting that targeting inflammation could represent a promising therapeutic approach for MDD. MDD frequently co-occurs with other pathological conditions, including cardiometabolic, autoimmune, and chronic pain disorders. These comorbidities further complicate MDD treatment and contribute to reduced antidepressant efficacy. Like MDD, these disorders are characterized by a strong inflammatory component, and several cytokines and pro-inflammatory mechanisms altered in MDD are also found in these comorbid conditions. This narrative review explores inflammation as a shared biological mechanism in MDD and its most frequent comorbidities, to provide a comprehensive understanding of the interplay between inflammation and these comorbid conditions. Persistent low-grade inflammation may help explain the high rate of bidirectional co-occurrence between MDD and its comorbidities. Moreover, it may represent a target for better understanding the molecular mechanisms driving this co-occurrence, potentially contributing to the development of tailored treatment and improving antidepressants response rates. Full article

Social media provides a vital arena for rare disease (RD) communities, fostering support, advocacy, and knowledge sharing. Rare Disease Day generates a large-scale online conversation, yet previous research has relied mainly on static, cross-sectional snapshots. This study captures the longitudinal evolution of the Spanish-language Twitter debate around Rare Disease Day across a fixed yearly window (1 February to 15 March) from 2008 to 2023. After filtering for Spanish-language posts, a corpus of 308,823 tweets (72,740 originals) was analyzed. We combined hashtag frequency analysis to assess topic salience with social network analysis (SNA) of co-occurrence networks to identify central thematic clusters. Results show progression from early generic expressions to increasingly deliberate, action-oriented communication, reflecting a shift towards empowered activism. A headline finding is the structural centrality and persistence of the hashtag #investigación (#research), underscoring the community’s enduring call for scientific progress. SNA further revealed the difference between transient virality—often linked to political or celebrity-driven hashtags—and the stable, identity-related topics at the core of the debate. Longitudinal hashtag analysis, particularly using SNA, provides a powerful tool to identify stable priorities of online health communities beyond transient media noise. Full article